There has recently been dramatic growth in the number and volume of securities transactions executed over electronic communication networks (ECNs). On an ECN, market participants can access market information and communicate orders to a central order-execution mechanism. The explosive growth of retail and professional trading over the Internet has extended access to ECN liquidity to new and distinct classes of market participants beyond traditional market professionals and institutional investors. ECNs have also developed the ability to manage and execute many order types and to dynamically manage complex baskets of many orders in near real-time. These developments have made ECNs ubiquitous, fast, and flexible and have created a dynamic market environment with a rapidly changing book of executable orders.
In a typical ECN trading situation where a market participant has a buying or selling interest, liquidity conditions can make it very difficult to determine the market impact of a potential order. The critical questions are whether sufficient counter-interest exists in the book of executable orders or quotes to complete the potential order, and if so, at what average price that order would execute (referred to herein as the order's “market impact”). Existing methods of generating and displaying liquidity information and market impact analysis fail to adequately provide an answer to these questions. For example, a display of the inside quote (the price and volume of the highest bid and lowest offer available in the book of executable orders) only provides an answer if the potential trade is of a smaller size than the displayed counter-interest.
There has been a dramatic increase in the number of market participants who instead perform this analysis from a display of the full depth of quote in a securities market, because ECNs freely disseminate such information and more market participants are paying for the information from markets such as NASDAQ. A display of the full depth of quote, such as the typical display depicted in FIG. 3, may provide enough information to manually calculate market impact, but as a practical matter it is difficult, if not impossible, to manually perform such calculations in real-time. Use of the order array depicted in FIG. 3 to deduce market impact requires a trader to process large quantities of rapidly changing raw numeric data while simultaneously making broader transaction decisions. This difficulty is compounded if the trader is analyzing several order arrays for various securities of interest. The process is further complicated by non-obvious non-linear execution rules relating to orders with minimum volume restrictions. Also, the equity market as a whole has experienced a significant upward trend in both trading volume and volatility over the past five years, adding to the kinetic pace of change in instantaneous liquidity and therefore increasing the need for rapid market impact analysis. Furthermore, if decimal equity pricing is adopted in the United States (as planned), the order array depicted in FIG. 3 will become even more disperse and difficult to analyze.
A known approach to analyzing market impact is disclosed in U.S. Pat. No. 5,924,083, to Silverman, et al., which discloses a trading system that attempts to hypothetically fill a potential order using the executable order book, and displays to the user the weighted average price at which the potential order would be executed. This provides numerical display of average prices and requires iterative user input to generate estimated execution prices for multiple potential trade sizes; in an alternate embodiment, the system provides numerical display of average price for a handful of system-defined standard sizes. In either embodiment, the system disclosed in Silverman et al. fails to provide the average price as a piecewise-continuous function of trade size—at most it gives a sample of numerical values, without any graphical display or integration with other market information.
Another important factor in trade decision-making is intra-day market activity. Traders constantly monitor sources of market activity data to locate opportunities presented by changes in demand and liquidity patterns, short term imbalances of supply and demand, or other developments that can be identified by keeping abreast of market activity. Existing sources of this information typically express total volume traded or price action (whether last trade or best bid and offer) as a function of time, or both together. These systems are well known in the art and can be found, e.g., at Bloomberg.com or Finance.Yahoo.com. Existing market information systems simply access trade history and do not identify or classify the executing brokers on either side of the transaction. The typical combined price volume display described above fails to separate the trading activity of different classes of market participants, which often react very differently both temporally and substantively to market price action and information dissemination. The ability to separately view trading activity by market participant class produces a much clearer picture of market dynamics, and analysis of the reaction patterns of such market participant classes can have crucial predictive value.